广东工业大学学报 ›› 2022, Vol. 39 ›› Issue (05): 68-74.doi: 10.12052/gdutxb.220056
杨翊卓, 代伟
Yang Yi-zhuo, Dai Wei
摘要: 随着工业互联网的发展,为避免网络拥塞,控制系统正在由时间触发向事件触发控制方向发展。本文针对一类多速率工业过程,将模型预测控制与提升技术和事件触发技术相结合,提出了事件触发机制下的多速率模型预测控制方法。该方法首先采用提升技术解决多速率问题,进而采用模型预测控制(Model Predictive Control, MPC)方法设计控制器以跟踪设定值;在此基础上,设计保证系统稳定的事件触发机制,使控制器仅在违反触发机制时更新。通过对典型工业磨矿过程仿真实验来验证本文所提方法的有效性,结果表明所提方法能够有效减少信道资源的占用和计算负载,为工业互联网环境下工业过程控制器设计提供了新的方法。
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[1] 周济. 智能制造?“中国制造2025”的主攻方向[J]. 中国机械工程, 2015, 26(17): 2273-2284. ZHOU J. Intelligent Manufacturing—main direction of “Made in China 2025” [J]. China Mechanical Engineering, 2015, 26(17): 2273-2284. [2] 周琪, 陈广登, 鲁仁全, 等. 基于干扰观测器的输入饱和多智能体系统事件触发控制[J]. 中国科学: 信息科学, 2019, 49(011): 1502-1516. ZHOU Q, CHEN G D, LU R Q, et al. Disturbance observer based event-triggered control for multi-agent systems with input saturation [J]. Sci Sin Inform, 2019, 49(011): 1502-1516. [3] LI Y X, YANG G H. Model-based adaptive event-triggered control of strict-feedback nonlinear systems [J]. IEEE transactions on neural networks and learning systems, 2017, 29(4): 1033-1045. [4] HAN X, ZHAO X, SUN T, et al. Event-triggered optimal control for discrete-time switched nonlinear systems with constrained control input [J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2020(99): 1-10. [5] CHENG J, LIANG L D, PARK J H. A dynamic event-triggered approach to state estimation for switched memristive neural networks with nonhomogeneous sojourn probabilities [J]. IEEE Transactions on Circuits and Systems I:Regular Papers., 2021, 68(12): 4924-4934. [6] XU B, LIU X, WANG H. Event-triggered control for nonlinear systems via feedback linearization [J]. International Journal of Control, 2020(2): 1-19. [7] HAN Y C, LIAN J. Periodic event-triggered and self-triggered control of singular system under stochastic cyber-attacks [J]. IET Control Theory & Applications, 2020, 14(19): 156-165. [8] HASHIMOTO K, ADACHI S, DIMAROGONAS D V. Event-triggered intermittent sampling for nonlinear model predictive control [J]. Automatica, 2017, 81: 148-155. [9] HEEMELS W P M H, DONKERS M C F. Model-based periodic event-triggered control for linear systems [J]. Automatica, 2013, 49(3): 698-711. [10] DONKERS M C F, HEEMELS W P M H. Output-based event-triggered control with guaranteed L∞-gain and improved and decentralized event-triggering [J]. IEEE Transactions on Automatic Control, 2012, 57(6): 1362-1376. [11] LU Q, SHI P, LIU J, et al. Model predictive control under event-triggered communication scheme for nonlinear networked systems [J]. Journal of the Franklin Institute, 2019, 356(5): 2625-2644. [12] WANG X F, MICHAEL D L. Event-triggering in distributed networked control systems [J]. IEEE Transactions on Automatic Control, 2011, 56(3): 586-601. [13] POSTOYAN R, TABUADA P, NESIC D, et al. A framework for the event-triggered stabilization of nonlinear systems [J]. IEEE Transactions on Automatic Control, 2015, 60(4): 982-996. [14] WEI M, LI Y X, TONG S. Event-triggered adaptive neural control of fractional-order nonlinear systems with full-state constraints [J]. Neurocomputing, 2020, 412: 320-326. [15] WANG X F, MICHAEL D L. On event design in event-triggered feedback systems [J]. Automatica, 2011, 47(10): 2319-2322. [16] ZHANG X M, HAN Q L, ZHANG B L. An overview and deep investigation on sampled-data-based event-triggered control and filtering for networked systems [J]. IEEE Transactions on Industrial Informatics, 2017, 13(1): 4-16. [17] LU Q, SHI P, WU L G, et al. Event-triggered estimation and model predictive control for linear systems with actuator fault [J]. IET Control Theory & Applications, 2020, 14(16): 2406-2412. [18] ZHAO F Y, JIANG Z P, LIU T F, et al. Event-triggered adaptive optimal control with output feedback: an adaptive dynamic programming approach [J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(11): 5208-5221. [19] ZHAO F Y, GAO W N, LIU T F. Learning-based event-triggered adaptive optimal output regulation of linear discrete-time systems[C]//Data Driven Control and Learning Systems Conference (DDCLS). Suzhou: IEEE, 2021: 1516-1521. [20] SAHOO A, HAO X, JAGANNATHAN S. Near optimal event-triggered control of nonlinear discrete-time systems using neuro dynamic programming [J]. IEEE Transactions on Neural Networks & Learning Systems, 2016, 27(9): 1801-1815. [21] 代伟, 陆文捷, 付俊, 等. 工业过程多速率分层运行优化控制[J]. 自动化学报, 2019, 45(10): 1946-1959. DAI W, LU W J, FU J, et al. Multi-rate layered optimal operational control of industrial processes [J]. Acta Automatica Sinica, 2019, 45(10): 1946-1959. [22] 杨彬, 周琪, 曹亮, 等. 具有指定性能和全状态约束的多智能体系统事件触发控制[J]. 自动化学报, 2019, 45(8): 1527-1535. YANG B, ZHOU Q, CAO L, et al. Event-triggered control for multi-agent systems with prescribed performance and full state constraints [J]. Acta Automatica Sinica, 2019, 45(8): 1527-1535. [23] CHEN X S, YANG J, LI S H. Disturbance observer based multi-variable control of ball mill grinding circuits [J]. Journal of Process Control, 2009, 19(7): 1205-1213. |
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